Mohammed Abdulkreem Mohammed - Department of Anesthesia Techniques, Al-Noor University College, Bartella, Iraq
Drai Ahmed Smait - The University of Mashreq, Iraq
Mustafa Al-Tahai - Medical technical college/ Al-Farahidi University, Baghdad, Iraq
Israa S. Kamil - Medical Laboratories Techniques Department, Al-Mustaqbal University College, Babylon, Iraq
Kadhum Al-Majdi - Department of biomedialc engineering, Ashur University College, Baghdad, Iraq
Shahad K. Khaleel - Al-Esraa University College, Baghdad, Iraq

Through the use of malware, particularly JavaScript, cybercriminals have turned online applications into one of their main targets for impersonation. Detection of such dangerous code in real-time, therefore, becomes crucial in order to prevent any harmful action. By categorizing the salient characteristics of the malicious code, this study suggests an effective technique for identifying malicious Java scripts that were previously unknown, employing an interceptor on the client side. By employing the wrapper approach for dimensionality reduction, a feature subset was generated. In this paper, we propose an approach for handling the malware detection task in imbalanced data situations. Our approach utilizes two main imbalanced solutions namely, Synthetic Minority Over Sampling Technique (SMOTE) and Tomek Links with the object of augmenting the data and then applying a Deep Neural Network (DNN) for classifying the scripts. The conducted experiments demonstrate the efficient performance of our approach and it achieves an accuracy of ۹۴.۰۰%.

malware detection, Imbalanced Data, Convolutional neural networks, SMOTE, Tomek Links